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【导读】Christine Doig是Anaconda公司的高级数据科学家。没错Anaconda就是那个著名的Python科学计算与发行管理软件。Christine Doig从最基本的强化学习概念开始介绍强化学习Python实践经验,并以强化学习中的经典任务--Cartpole问题作为学习的入门例子,讲解从环境搭建、模型训练再到最后的效果评估的结果。
▌简介
Cartpole描述的问题可以认为是:在一辆小车上竖立一根杆子,然后给小车一个推或者拉的力,使得杆子尽量保持平衡不滑倒。
更详细的描述可参见openai官网上关于Cartpole问题的解释:https://gym.openai.com/envs/CartPole-v0
▌强化学习用到的python库
OpenAI
Gym: Toolkit for developing and comparing reinforcement learningalgorithms. MIT License, Last commit: November 2017
baselines: high-quality implementations of reinforcement learning algorithms,MIT License, Last commit: November 2017
TensorForce, A TensorFlow library for applied reinforcement learning, Apache 2,Last commit: November 2017
DeepRL, Highly modularized implementation of popular deep RL algorithms byPyTorch, Apache 2 License, Last commit: November 2017
RLlab, a framework for developing and evaluating reinforcement learningalgorithms, MIT License, Last commit: July 2017
AgentNet, Python library for deep reinforcement learning usingTheano+Lasagne, MIT License, Last commit: August 2017
RLPy, the Reinforcement Learning Library for Education and Research,3-Clause BSD License, Last commit: April 2016.
PyBrain, the Python Machine Learning Library, 3-Clause BSD License, Lastcommit: March 2016.
▌强化学习资源
Reinforcement Learning courseby David Silver
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
https://blog.acolyer.org/2017/11/17/mastering-the-game-of-go-without-humanknowledge/
https://keon.io/deep-q-learning/
https://rishav1.github.io/reinlearning/2017/01/05/simple-swarm-intelligenceoptimization-for-cartpole-balancing-problem.html
AlphaGo Zero's win, what itmeans, Fast Forward Labs: http:// blog.fastforwardlabs.com/2017/10/25/alphago-zero.html
更多可以查看专知以前推出的强化学习荟萃资料:
【专知荟萃23】深度强化学习RL知识资料全集(入门/进阶/论文/综述/代码/专家,附查看)
▌PPT内容
参考链接:
https://speakerdeck.com/chdoig/rl-pytexas-2017
▌特别提示-Python强化学习实战 PPT下载:
请关注专知公众号(扫一扫最下面专知二维码,或者点击上方蓝色专知),
后台回复“RLP” 就可以获取PPT下载链接~
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